Tag Archives: Enterprise Data Architecture

Foundational Architecture Decisions in a Financial Services Data Platform

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This article defines a comprehensive architectural doctrine for modern Financial Services data platforms, separating precursor decisions (what must be true for trust and scale) from foundational decisions (how the platform behaves under regulation, time, and organisational pressure). It explains why ingestion maximalism, streaming-first eventual consistency, transactional processing at the edge, domain-first design, and freshness as a business contract are non-negotiable in FS. Through detailed narrative and explicit anti-patterns, it shows how these decisions preserve optionality, enable regulatory defensibility, support diverse communities, and prevent the systemic failure modes that quietly undermine large-scale financial data platforms.

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Time, Consistency, and Freshness in a Financial Services Data Platform

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This article explains why time, consistency, and freshness are first-class architectural concerns in modern Financial Services data platforms. It shows how truth in FS is inherently time-qualified, why event time must be distinguished from processing time, and why eventual consistency is a requirement rather than a compromise. By mapping these concepts directly to Bronze, Silver, Gold, and Platinum layers, the article demonstrates how platforms preserve historical truth, deliver reliable current-state views, and enforce freshness as an explicit business contract rather than an accidental outcome.

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Databricks vs Snowflake vs Microsoft Fabric: Positioning the Future of Enterprise Data Platforms

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This article extends the Databricks vs Snowflake comparison to include Microsoft Fabric, exploring the platforms’ philosophical roots, architectural approaches, and strategic trade-offs. It positions Fabric not as a direct competitor but as a consolidation play for Microsoft-centric organisations, and introduces Microsoft Purview as the governance layer that unifies divergent estates. Drawing on real enterprise patterns where Databricks underpins engineering, Fabric drives BI adoption, and functional teams risk fragmentation, the piece outlines the “Build–Consume–Govern” model and a phased transition plan. The conclusion emphasises orchestration across platforms, not choosing a single winner, as the path to a governed, AI-ready data estate.

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Databricks vs Snowflake: A Critical Comparison of Modern Data Platforms

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This article provides a critical, side-by-side comparison of Databricks and Snowflake, drawing on real-world experience leading enterprise data platform teams. It covers their origins, architecture, programming language support, workload fit, operational complexity, governance, AI capabilities, and ecosystem maturity. The guide helps architects and data leaders understand the philosophical and technical trade-offs, whether prioritising AI-native flexibility and open-source alignment with Databricks or streamlined governance and SQL-first simplicity with Snowflake. Practical recommendations, strategic considerations, and guidance by team persona equip readers to choose or combine these platforms to align with their data strategy and talent strengths.

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